• Journal of KIISE:Databases
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• v.36 no.3
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• pp.169-179
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• 2009

Communication log data consist of communication events such as sending and receiving e-mail or instance message and visiting web sites, etc. Many countries including USA and EU enforce the retention of these data on the communication service providers for the purpose of investigating or detecting criminals through the Internet. Because size of the retained data is very large, the efficient method for extracting valuable information from the data is needed for Law Enforcement Authorities to use the retained data. This paper defines the Interactive Communication Sequence Patterns(ICSPs) that is the important information when each communication event in communication log data consists of sender, receiver, and timestamp of this event. We also define a Mining(FDICSP) problem to discover such patterns and propose a method called Fast Discovering Interactive Communication Sequence Pattern(FDICSP) to solve this problem. FDICSP focuses on the characteristics of ICS to reduce the search space when it finds longer sequences by using shorter sequences. Thus, FDICSP can find Interactive Communication Sequence Patterns efficiently.

• Journal of Sasang Constitutional Medicine
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• v.24 no.4
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• pp.28-39
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• 2012

Objectives : This study is aimed to develop the algorithm to diagnose Taeeumin's symptomology, by the method of literature research on Sasang Constitutional Medicine. Methods : Applying the sequential differentiations of Taeeumin's symptomology, or exterior-interior disease differentiation, favorable-unfavorable pattern differentiation, and mild-severe-dangerous-urgent pattern differentiation, "Donguisusebowon" and related literatures have been reviewed. Results and Conclusions : 1) 1st step: Taeeumin's exterior pattern and interior pattern are differentiated by the indexes of whole-body cold or heat pattern, sweating, and facial complexion. 2) 2nd step: The favorable pattern of the Taeeumin's exterior disease can be detected by indexes of the existence of fever, generalized pain while the unfavorable one by indexes of the abnormal condition of digestion and feces, and fearful throbbing. The favorable pattern of the Taeeumin's interior disease can be diagnosed based on indexes of eye pain, dry nose, dry throat, and heat symptoms that occur in various parts of the body, while the unfavorable one by indexes of thirsty, urination, feces and specific symptoms which can be induced by dryness. And in the both unfavorable patterns the dark complexion on the faces is revealed. 3) 3rd step: The mild-severe patterns of the favorably exterior disease are differentiated in terms of the condition of fever, while the mild-severe patterns of the favorably interior disease are in differentiated based on whether abnormal symptoms are revealed in the gastrointestinal tract. Both of the unfavorably dangerous-urgent patterns in exterior and interior diseases are differentiated by the symptoms such as tinnitus, dim vision, weakness of legs and back pain, and lack of strength in legs and thighs.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.281-290
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• 2003

Co Evolutionary Structural Design(CESD) Framework is presented, which can deal with the load design and structural topology design simultaneously. The load design here is the exploration algorithm that finds the critical load patterns of the given structure. In general, the load pattern is a crucial factor in determining the structural topology and being selected from the experts어 intuition and experience. However, if any of the critical load patterns would be excluded during the process of problem formation, the solution structure might show inadequate performance under the load pattern. Otherwise if some reinforcement method such as safety factor method would be utilized, the solution structure could result in inefficient conservativeness. On the other hand, the CESD has the ability of automatically finding the most critical load patterns and can help the structural solution evolve into the robust design. The CESD is made up of a load design discipline and a structural topology design discipline both of which have the fully coupled relation each other. This coupling is resolved iteratively until the resultant solution can resist against all the possible load patterns and both disciplines evolve into the solution structure with the mutual help or competition. To verify the usefulness of this approach, the 10 bar truss and the jacket type offshore structure are presented. SORA(Sequential Optimization & Reliability Assessment) is adopted in CESD as a probabilistic optimization methodology, and its usefulness in decreasing the computational cost is verified also.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.

• Computers and Concrete
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• v.17 no.3
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• pp.353-386
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• 2016

Realistic assessment of the performance of reinforced concrete structural members like columns is needed for designing new structures or maintenance of the existing structural members. This assessment requires analytical capability of employing proper material models and cyclic rules and considering various load and displacement patterns. A computer application was developed to analyze the non-linear, cyclic flexural performance of reinforced concrete structural members under various types of loading paths including non-sequential variations in axial load and bi-axial cyclic load or displacement. Different monotonic material models as well as hysteresis rules, were implemented in a fiber-based moment-curvature and in turn force-deflection analysis, using proper assumptions on curvature distribution along the member, as in plastic-hinge models. Performance of the program was verified against analytical results by others, and accuracy of the analytical process and the implemented models were evaluated in comparison to the experimental results. The computer application can be used to predict the response of a member with an arbitrary cross section and various type of lateral and longitudinal reinforcement under different combinations of loading patterns in axial and bi-axial directions. On the other hand, the application can be used to examine analytical models and methods using proper experimental data.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.251-260
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• 2008

In internet being a repository of massive information, we easily may not find our desired information, this issue also exists in e-commerce which gets rapid growth. In most of e-commerce sites, the methods furnishing information have been made use of statistical analysis or simple process by category-oriented, but these can't represent diverse correlation among products information and also hardly reflect users' purchasing patterns precisely. In this thesis, we propose more efficient web mining ways for discovering interesting information using effective clustering in e-commerce, which get achieved more suitable relationship among products information using both sequential patterns and association rules in category-independent, and experiments show the efficiency of our proposed methods. And we propose search using effective clustering rapidly.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.

• IE interfaces
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• v.16 no.4
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• pp.507-514
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• 2003

In personal communication systems location management of mobile terminals is required to connect incoming calls to the mobile terminals. Investigation of effective location update and paging strategies is important to reduce the cost of location management. In this study, we propose a paging strategy considering the mobile terminals' residence patterns. The first paging area is characterized by the set of cells in the location area where mobile terminals usually stay for a significant period of time. The rest of cells in the location area are included into the second paging area. To reduce the paging cost the first paging area is decided by reflecting various residence patterns of mobile terminals. For an incoming call, the sequential paging is performed in the order of the paging area. Thus the paging cost is greatly reduced, especially when the called mobile terminal is located in the first paging area. The proposed strategy is expected to be more effective than an existing strategy that uses the anchor cell in the location area.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.327-330
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• 2004

Because of its effectiveness for the PD(partial discharge) pattern recognition, PSA(Pulse Sequence Analysis) has been considered as a new analytic method instead of conventional PRPDA(Phase Resolved Partial Discharge Analysis). However, PSA has a big problem that can misanalyze patterns in case of data missing resulting from poor sensitivity because it analyses the correlation between sequential pulses, which leads to hesitate to apply it to on-site. Therefore, in this paper, the problems of PSA such as data missing and noise adding cases were investigated. For the purpose, PD data obtained from various defects including noise adding data were used and analysed, The result showed that both cases can cause fatal errors in recognizing PD patterns. In case of the data missing, the error depends on the kinds of defect and the degree of degradation. Also, it could be noticed that the error due to adding noises was larger than that due to some data missing.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.